TY - JOUR
ID - st0515
A1 - Schwarz, C.
TI - ldagibbs: A command for topic modeling in Stata using latent Dirichlet allocation
JF - Stata Journal
PB - Stata Press
CY - College Station, TX
Y1 - 2018
VL - 18
IS - 1
SP - 101
EP - 117
KW - ldagibbs
KW - machine learning
KW - latent Dirichlet allocation
KW - Gibbs sampling
KW - topic model
KW - text analysis
UR - http://www.stata-journal.com/article.html?article=st0515
AB - In this article, I introduce the ldagibbs command, which implements
latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most
popular machine-learning topic model. Topic models automatically cluster text
documents into a user-chosen number of topics. Latent Dirichlet allocation
represents each document as a probability distribution over topics and
represents each topic as a probability distribution over words. Therefore,
latent Dirichlet allocation provides a way to analyze the content of large
unclassified text data and an alternative to predefined document
classifications.
ER -